Innovation Through AI Integration: A Study of AI Technology Adoption for the Maritime Inspection and Consulting Sector using Soft System Methodology
Article Sidebar
Main Article Content
Abstract
The maritime industry, a global powerhouse for trade and transportation, is on the cusp of a transformative era driven by Artificial Intelligence (AI). This paper explores the potential of AI to revolutionize various aspects of the maritime sector, from navigation and route optimization to predictive maintenance and enhanced safety. This paper will study AI implementation in the inspection and consulting industry. This AI will help speed up the maritime industry's regulatory compliance process and maintenance services. The study showed four main stakeholders in AI implementation: Operational experts, managers, clients, and the AI team. Each stakeholder has a crucial role in successfully implementing AI in this industry. This study will map out each stakeholder's interest using SSM, giving a clear picture of what each stakeholder wants and their apprehensions. The result of this study is some conditions that need to be met by each stakeholder for the implementation of AI in this sector to be successful.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Bradbury, H. (2015). The SAGE Handbook of Action Research. SAGE Publications Ltd. https://doi.org/10.4135/9781473921290
Challender, S. (2000). Systems Thinking, Systems Practice. By Peter B. Checkland. Published by John Wiley, Chichester, UK, 1981, 330 pp., ISBN 0 471 27911 0 (republished 1999 in paperback, with a 30-year retrospective). From a practitioner perspective. Systems Research and Behavioral Science, 17(S1), S78–S80. https://doi.org/https://doi.org/10.1002/1099-1743(200011)17:1+<::AID-SRES384>3.0.CO;2-N
Vidal, R. V. V. (2005). J. Rosenhead and J. Mingers (Eds.), Rational Analysis for a Problematic World Revisited, Problem Structuring Methods for Complexity, Uncertainty and Conflict, Wiley, Chichester, 2001, xviii+366 pages, £ 22.50. European Journal of Operational Research, 161(2), 582–583. https://doi.org/10.1016/j.ejor.2004.03.004
Wheeler, F., Checkland, P., & Scholes, J. (2000). Soft Systems Methodology in Action: Including a 30-Year Retrospective. The Journal of the Operational Research Society, 51, 648. https://doi.org/10.2307/254201
Simon, G.., Dinardo, C.., Takahashi, Koichi., Cascone, T.., Powers, Cynthia A.., Stevens, Rick J., Allen, Joshua., Antonoff, M.., Gomez, D.., Keane, P.., Saiz, Fernando Jose Suarez., Nguyen, Q.., Roarty, Emily B.., Pierce, S.., Zhang, Jianjun., Barnhill, Emily Hardeman., Lakhani, Kate., Shaw, K.., Smith, Brett., Swisher, S.., High, Rob., Futreal, P.., Heymach, J.., & Chin, L.. (2018). Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care. The oncologist, 24 6, 772–782. http://doi.org/10.1634/theoncologist.2018-0257
Șerban, A.., & Lytras, Miltiadis Demetrios. (2020). Artificial Intelligence for Smart Renewable Energy Sector in Europe—Smart Energy Infrastructures for Next Generation Smart Cities. IEEE Access, 8, 77364-77377. http://doi.org/10.1109/ACCESS.2020.2990123
Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., Jadeyegowda, M., & Kiesecker, J. (2022). Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India. Sustainability. http://doi.org/10.3390/su14127154
Lyon, A. R., Pullmann, M., Whitaker, K., Ludwig, K. A., Wasse, J.K., & McCauley, E. (2019). A Digital Feedback System to Support Implementation of Measurement-Based Care by School-Based Mental Health Clinicians. Journal of Clinical Child & Adolescent Psychology, 48, S168-S179. http://doi.org/10.1080/15374416.2017.1280808
Regona, M., Yigitcanlar, T, Xia, B., & Li, R.. (2022). Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review. Journal of Open Innovation: Technology, Market, and Complexity. http://doi.org/10.3390/joitmc8010045
Dora, M., Kumar, A., Mangla, S., Pant, A., & Kamal, M. (2021). Critical success factors influencing artificial intelligence adoption in food supply chains. International Journal of Production Research, 60, 4621–4640. http://doi.org/10.1080/00207543.2021.1959665
Tahiru, F. (2021). AI in Education: A Systematic Literature Review. J. Cases Inf. Technol. , 23, 1–20. http://doi.org/10.4018/jcit.2021010101
Schaefer, Cindy., Lemmer, K.., Kret, Kret Samy., Ylinen, Maija., Mikalef, Patrick., & Niehaves, Björn. (2021). Truth or Dare? - How can we influence the Adoption of Artificial Intelligence in Municipalities?. 1-10. http://doi.org/10.24251/HICSS.2021.286
Osamy, W., Khedr, A., Salim, A., AlAli, A., & El-sawy, A. (2022). Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation, and Dissemination Challenges in Wireless Sensor Networks: A Review. Electronics. http://doi.org/10.3390/electronics11030313
Rawindaran, Nisha., Jayal, Ambikesh., & Prakash, E.. (2021). Machine Learning Cybersecurity Adoption in Small and Medium Enterprises in Developed Countries. Comput. , 10, 150. http://doi.org/10.3390/computers10110150
Marinakis, Vangelis., Koutsellis, Themistoklis., Nikas, A.., & Doukas, H.. (2021). AI and Data Democratisation for Intelligent Energy Management. Energies. http://doi.org/10.3390/EN14144341
Schlögl, Stephan., Postulka, Claudia., Bernsteiner, R.., & Ploder, Christian. (2019). Artificial Intelligence Tool Penetration in Business: Adoption, Challenges, and Fears. , 259-270. http://doi.org/10.1007/978-3-030-21451-7_22